International Journal of Value Chain Management (9 papers in press)

Regular Issues

• Assessing Methodological Diversity in Multidisciplinary Supply Chain Research: Insights from Recent Literature  
  by Shivendra Dwivedi, Vinod Mishra 
  Abstract: This review paper reviews the current state of multidisciplinary research in supply chains, focusing on sustainability, coordination contracts, and green practices. By integrating insights from various disciplines, the study compiles findings on the effectiveness of coordination contracts in promoting sustainable supply chains. A systematic multi-stage literature review has been conducted using Science Direct, Springer, and Taylor & Francis databases, covering publications from 2019 to 2023. Through successive keyword filtering, relevant papers have selected for in-depth qualitative analysis. The literature is analysed from three perspectives: environmental, social, and economic sustainability. The review identifies key research gaps, including limited use of real-world data, insufficient empirical evidence, narrow methodological diversity, and an excessive focus on manufacturing sectors. These findings suggest avenues for future research to explore broader contexts, employ diverse methodologies, and strengthen practical relevance in sustainable supply chain coordination.
  Keywords: Supply Chain; Sustainability; Coordination Contract; Green Supply Chain.
  DOI: 10.1504/IJVCM.2025.10075583
   
  
• Analysing Regional Potential in the Emerging Green Hydrogen Global Value Chain  
  by Janne Anttila, Tatu Hyvärinen, Pekka Tervonen, Harri Jouni Olavi Haapasalo 
  Abstract: The objective of this study is to develop and demonstrate a systematic approach for assessing regional potential to participate in global value chains (GVCs), using the emerging green hydrogen value chain as the empirical context. The study introduces a six-step assessment framework comprising (1) defining the value chain and regional scope, (2) indicator selection, (3) defining weights and constraints, (4) data collection, (5) development of the calculation model, and (6) analysis. The framework is applied to Northern Ostrobothnia, Finland, to evaluate municipal-level capabilities across different segments of the green hydrogen value chain. The results reveal substantial variation between municipalities and enable the identification of four distinct regional roles associated with distinct regional development trajectories. The main contribution of the study is the proposed assessment method, the benefit of which lies in its broad applicability to examining regional potential across GVCs beyond the hydrogen context.
  Keywords: Value chain; global value chain; GVC; emerging value chain; green hydrogen; clean hydrogen; economic relatedness; upgrading; smart specialisation; integration; segmentation; regional capabilities.
  DOI: 10.1504/IJVCM.2026.10077161
   
  
• Exploring the Effects of Modularity, Mass Customization, and Postponement on Outsourcing  
  by Ashish Thatte, Vikas Agrawal, Parag Dhumal 
  Abstract: This research examines the relationships among product modularity, process modularity, mass customisation, postponement, and outsourcing within supply chain management (SCM). Using survey data from 294 senior supply chain executives across multiple industries, the analysis investigates how product and process modularity influence mass customisation and postponement, how mass customisation affects postponement and outsourcing, and how postponement impacts outsourcing. The findings indicate that both product and process modularity significantly improve firms mass customisation capabilities. Mass customisation, in turn, positively influences postponement, which subsequently drives outsourcing. Contrary to the initial hypotheses, product and process modularity do not directly affect postponement; rather, their influence is mediated through mass customisation. Similarly, the effect of mass customisation on outsourcing occurs indirectly through postponement. Firms can leverage modular product and process designs to build mass-customisation capabilities, enabling effective postponement and optimised outsourcing, thereby enhancing operational flexibility, reducing costs, and improving responsiveness in dynamic global supply chains.
  Keywords: Outsourcing; Postponement; Mass customization; Process Modularity; Product Modularity.
  DOI: 10.1504/IJVCM.2026.10077334
   
  
• Supplier Selection and Order Allocation in Smart Manufacturing Paradigm: An ANP-TOPSIS Approach  
  by Roghaye Bazoubandi, Masoud Rahiminezhad Galankashi 
  Abstract: The primary aim of this research is to present an integrated Multi-Criteria Decision Making (MCDM) and bi-objective mathematical model for supplier selection and order allocation in smart manufacturing paradigm. Although there is a substantial amount of research on supplier selection, order allocation and its associated tools, the topic of supplier selection and order allocation in smart manufacturing paradigm has received less attention. To address this deficiency, this study is carried out in three phases as detailed below. Firstly, an initial review on smart manufacturing paradigm criteria is completed using earlier research. Then, an Analytic Network Process (ANP) and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) methodologies are applied to choose and evaluate suppliers. Finally, a bi-objective mathematical model is applied to identify optimal order allocation to each supplier. The model verifies, validates, and undergoes a sensitivity analysis to offer managerial recommendations based on various parameter values.
  Keywords: supplier selection; order allocation; smart manufacturing; ANP; TOPSIS.
  DOI: 10.1504/IJVCM.2026.10077434
   
  
• Integrating the social pillar of sustainability in supply chain management: a Review  
  by Giannis T. Tsoulfas, Foivos Anastasiadis, Jurgita Paužuolienė, Ilvija Pikturnaitė 
  Abstract: This paper examines the integration of social sustainability into supply chain management (SCM) through bibliometric and content analysis of 279 papers extracted from the Scopus database. The objective is to identify research trends driving organisational adoption of socially responsible practices. Analysis reveals four key research trends: 1) exploring stakeholder roles, institutional pressures, and governance mechanisms; 2) employing decision-support tools for assessing social performance; 3) analysing digitalisations role in monitoring labour practices and ensuring transparency; and 4) examining social supply chain practices impact on multiple sustainability dimensions. Key benefits include enhanced understanding of how ethical imperatives and competitive advantages drive social sustainability adoption, comprehensive frameworks for performance assessment, and evidence that social initiatives create synergies across environmental and economic objectives. These findings provide practical guidance for organisations implementing socially responsible supply chain strategies and address contributions to UN SDGs, particularly SDG1, SDG5, SDG8, SDG10, and SDG16.
  Keywords: Social Sustainability; Supply Chain Management; Stakeholder Collaboration; Social Performance; Decision-support; Digitalisation.
  DOI: 10.1504/IJVCM.2026.10077625
   
  
• The Impact of AI Adoption on Business Models and Value Chains: Insights from Italian Firms  
  by Grazia Garlatti Costa, Roberto Pugliese, Francesco Venier 
  Abstract: This study addresses a critical gap in understanding how artificial intelligence (AI) adoption reshapes business models and value chains. Integrating the Business Model Canvas and Porter's Value Chain frameworks, it provides a comprehensive analysis of AI's transformative effects while distinguishing between Predictive/Analytic AI and Generative AI. Drawing on survey data from 237 senior managers in Italian firms, the research reveals that 36.7% of firms have not yet implemented AI solutions, indicating a cautious adoption trajectory. Among adopters, AI significantly impacts Key Activities, Customer Relationships, Operations, and Technology Development, with Predictive/Analytic AI achieving deeper operational penetration while Generative AI remains predominantly experimental. The most critical barrier is the availability of skilled personnel, followed by strategic clarity and data quality concerns. This study contributes by proposing an integrated analytical framework, offering differentiated insights on AI types, and providing practical guidance for managers pursuing AI-driven business model innovation and value chain optimisation.
  Keywords: Artificial Intelligence; Generative AI; Italian Companies; Business Model Canvas; Porter’s Value Chain.
  DOI: 10.1504/IJVCM.2027.10078507
   
  
• Supply Chain Cost Drivers and Operational Performance of Food and Beverage Manufacturing Firms: Does Lean Manufacturing Matter?  
  by Edmond Yeboah Nyamah, Abdul-Nasir Abdallah Buabeng, Gloria K.Q. Agyapong, Evelyn Y. Nyamah 
  Abstract: This paper investigates the role of lean manufacturing as a moderator of the relationship between supply chain cost drivers and operational performance of food and beverage manufacturing firms. Through a survey approach, the paper used the census method to collect data from 110 food and beverage manufacturing firms. Using PLS-SEM, the direct and moderator analyses were examined. The results reveal that OP is positively and significantly influenced by IC (? = 0.390, p < 0.05), TC (? = 0.203, p < 0.05), and QC (? = 0.338, p < 0.05). The findings also show that LM significantly moderates the relationship between IC and OP (? = -0.425, p < 0.05) and QC and OP (? = 0.431, p < 0.033). However, LM does not moderate the relationship between TC and OP (? = 0.073, p < 0.533). The study gives theoretical, practical, and policy implications, and suggestions for future research.
  Keywords: Supply chain; cost drivers; lean manufacturing; operational performance; food and beverage; inventory; transportation; and quality.
  DOI: 10.1504/IJVCM.2027.10078675
   
  
• A Hybrid Linear Programming and Recurrent Neural Network Approach for Optimizing Perishable Supply Chains  
  by Prafulla Manoharan, Vinay Sharma 
  Abstract: This study presents an intelligent-hybrid-optimisation framework for improving the efficiency and sustainability of perishable food supply chains. The primary objective is to develop and validate a hybrid-linearprogramming recurrent neural network (LP-RNN) model integrated with value stream mapping (VSM) to minimise operational cost, reduce lead-time, and preserve product quality under uncertain demand conditions. Linear programming is employed to achieve optimal resource allocation and cost minimisation, while the recurrent neural network enables adaptive learning and classification of value-added and non-value-added activities across the supply-chain. Experimental results demonstrate that the proposed LP-RNN model shown better results amongst different alternatives and delivers superior solution diversity and optimisation performance and confirm that the proposed LP-RNN-based decision-support system provides a robust, scalable solution for reducing waste, improving delivery reliability, and enhancing operational efficiency in real-world perishable supply-chain environments. The framework offers significant practical value for logistics planners and supply-chain managers seeking intelligent and data-driven optimisation strategies.
  Keywords: Hybrid optimization; Linear programming; Recurrent neural network; Perishable supply chain; Value stream mapping; Intelligent decision-support system.
  DOI: 10.1504/IJVCM.2027.10078842
   
  
• Using Open-Source Intelligence and a Top Down/Bottom up Approach to Improve Supply Chain Risk Management in a Government Agency  
  by Rajkamal Kesharwani, Fred Hoffman 
  Abstract: A U S government entity sought to incorporate outside expertise when reassessing its approach to supply chain risk management (SCRM) Due to globalization, organizational supply chains often depend on primary, secondary, and tertiary suppliers located around the world To mitigate risk, the entity selected an academic team from Mercyhurst University that included professors experienced in intelligence, supply chain decomposition, and risk management and a student team experienced in intelligence collection, aggregation, and the use of structured analytic techniques The Mercyhurst team used OSINT tools, techniques, and tradecraft in a top-down and bottom-up approach to assess the government entity’s supply chain The top-down approach employed such competitive intelligence techniques as benchmarking and “Know Your Customer (KYC)” procedures, while the bottom-up approach examined the existing risk identification and categorization approaches used by the government entity’s subordinate organizations, standardized and quantified them, then used Root Cause Analysis to analyze threats and recommend mitigations.
  Keywords: Value chain management; supply chain risk management; open-source intelligence; intelligence collection; intelligence analysis; benchmarking.
  DOI: 10.1504/IJVCM.2027.10078983